The Artificial Intelligence Infrastructure Workshop

Explore how a data storage system works – from data ingestion to representation

Key Features

• Understand how artificial intelligence, machine learning, and deep learning are different from one another
• Discover the data storage requirements of different AI apps using case studies
• Explore popular data solutions such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3)

Book Description

Social networking sites see an average of 350 million uploads daily – a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one.

The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You’ll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you’ll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You’ll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you’ll gain hands-on experience with PyTorch. Finally, you’ll explore ways to run machine learning models in production as part of an AI application.

By the end of the book, you’ll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods.

What you will learn

• Get to grips with the fundamentals of artificial intelligence
• Understand the importance of data storage and architecture in AI applications
• Build data storage and workflow management systems with open source tools
• Containerize your AI applications with tools such as Docker
• Discover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS)
• Use the AWS CLI and AWS SDK to perform common data tasks

Who this book is for

If you are looking to develop the data storage skills needed for machine learning and AI and want to learn AI best practices in data engineering, this workshop is for you. Experienced programmers can use this book to advance their career in AI. Familiarity with programming, along with knowledge of exploratory data analysis and reading and writing files using Python will help you to understand the key concepts covered.

Table of Contents

1. Data Storage Fundamentals
2. Artificial Intelligence Storage Requirements
3. Data Preparation
4. Ethics of AI Data Storage
5. Data Stores: SQL and NoSQL Databases
6. Big Data File Formats
7. Introduction to Analytics Engine (Spark) for Big Data
8. Data System Design Examples
9. Workflow Management for AI
10. Introduction to Data Storage on Cloud Services (AWS)
11. Building an Artificial Intelligence Algorithm
12. Productionizing Your AI Applications